{
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    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Feature agglomeration\n\n\nThese images how similar features are merged together using\nfeature agglomeration.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
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      "outputs": [],
      "source": [
        "print(__doc__)\n\n# Code source: Ga\u00ebl Varoquaux\n# Modified for documentation by Jaques Grobler\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import datasets, cluster\nfrom sklearn.feature_extraction.image import grid_to_graph\n\ndigits = datasets.load_digits()\nimages = digits.images\nX = np.reshape(images, (len(images), -1))\nconnectivity = grid_to_graph(*images[0].shape)\n\nagglo = cluster.FeatureAgglomeration(connectivity=connectivity,\n                                     n_clusters=32)\n\nagglo.fit(X)\nX_reduced = agglo.transform(X)\n\nX_restored = agglo.inverse_transform(X_reduced)\nimages_restored = np.reshape(X_restored, images.shape)\nplt.figure(1, figsize=(4, 3.5))\nplt.clf()\nplt.subplots_adjust(left=.01, right=.99, bottom=.01, top=.91)\nfor i in range(4):\n    plt.subplot(3, 4, i + 1)\n    plt.imshow(images[i], cmap=plt.cm.gray, vmax=16, interpolation='nearest')\n    plt.xticks(())\n    plt.yticks(())\n    if i == 1:\n        plt.title('Original data')\n    plt.subplot(3, 4, 4 + i + 1)\n    plt.imshow(images_restored[i], cmap=plt.cm.gray, vmax=16,\n               interpolation='nearest')\n    if i == 1:\n        plt.title('Agglomerated data')\n    plt.xticks(())\n    plt.yticks(())\n\nplt.subplot(3, 4, 10)\nplt.imshow(np.reshape(agglo.labels_, images[0].shape),\n           interpolation='nearest', cmap=plt.cm.nipy_spectral)\nplt.xticks(())\nplt.yticks(())\nplt.title('Labels')\nplt.show()"
      ]
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